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Machine learning enhanced code optimization for high-level synthesis (ML-ECOHS)

Robert P. Munafo

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Tags: Compilers, Computer science, FPGA, HLS, Machine learning, OpenCL, Package, Thesis

June 2, 2024 by hgpu

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Ardhi Wiratama Baskara Yudha

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Security, Thesis

June 2, 2024 by hgpu

A Survey of Cloud-Based GPU Threats and Their Impact on AI, HPC, and Cloud Computing

Numaan Huq, Philippe Lin, Roel Reyes, Charles Perine

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Tags: AMD Radeon Pro V520, Artificial intelligence, ATI, Cloud, Computer science, CUDA, Deep learning, nVidia, OpenCL, Security, Tesla T4

June 2, 2024 by hgpu

An implementation of tensor product patch smoothers on GPU

Cu Cui, Paul Grosse-Bley, Guido Kanschat, Robert Strzodka

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Tags: CUDA, FEM, Finite element method, Mathematics, Numerical Analysis, nVidia, nVidia A100

June 2, 2024 by hgpu

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution

Guillaume Couairon, Christian Lessig, Anastase Charantonis, Claire Monteleoni

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Tags: Deep learning, Earth and Space Sciences, nVidia, nVidia A100, Weather prediction

May 26, 2024 by hgpu

Enabling full-speed random access to the entire memory on the A100 GPU

Alden Walker

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Tags: Computer science, nVidia, nVidia A100, Performance, PTX

May 26, 2024 by hgpu

GPU Implementations for Midsize Integer Addition and Multiplication

Cosmin E. Oancea, Stephen M. Watt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Performance, Programming Languages

May 26, 2024 by hgpu

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Roberto L. Castro, Diego Andrade, Basilio B. Fraguela

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia A100, Tesla T4

May 26, 2024 by hgpu

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Zhaodong Chen

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia A100, nVidia V100, Thesis

May 26, 2024 by hgpu

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

Bertil Schmidt, Andreas Hildebrandt

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Tags: Artificial intelligence, Bioinformatics, Biology, Chemistry, Neural networks, Quantum computing

May 20, 2024 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Machine learning enhanced code optimization for high-level synthesis (ML-ECOHS)

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

An implementation of tensor product patch smoothers on GPU

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution

Enabling full-speed random access to the entire memory on the A100 GPU

GPU Implementations for Midsize Integer Addition and Multiplication

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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